MetNet-2 encodes the lead time as a one-hot embedding with indices from 0 to 359 representing the range between 2 and 720 minutes [1]} and mapped into a continuous representation. Instead of feeding the lead time embedding only at the input of MetNet-2, the embedding is applied both an additive and multiplicative factor to each of the two convolutional layers in the residual blocks of MetNet-2 [2]}.
This ensures that the output of each convolutional layer now depends directly on lead time.
